Tag Archives: demography

Border fences make unequal neighbors

 

israelgazafence

 

There is one similarity between the Israel/Gaza crisis and the U.S. unaccompanied child immigrant crisis: National borders enforcing social inequality. When unequal populations are separated, the disparity creates social pressure at the border. The stronger the pressure, the greater the military force needed to maintain the separation.

To get a conservative estimate of the pressure at the Israel/Gaza border, I compared some numbers for Israel versus Gaza and the West Bank combined, from the World Bank (here’s a recent rundown of living conditions in Gaza specifically). I call that conservative because things are worse in Gaza than in the West Bank.

Then, just as demographic wishful thinking, I calculated what the single-state solution would look like on the day you opened the borders between Israel, the West Bank, and Gaza. I added country percentiles showing how each state ranks on the world scale (click to enlarge).

israelwbgaza

Israel’s per capita income is 6.2-times greater, its life expectancy is 6 years longer, its fertility rate is a quarter lower, and its age structure is reversed. Together, the Palestinian territories have a little more than half the Israeli population (living on less than 30% of the land). That means that combining them all into one country would move both populations’ averages a lot. For example, the new country would be substantially poorer (29% poorer) and younger than Israel, while increasing the national income of Palestinians by 444%. Israelis would fall from the 17th percentile worldwide in income, and the Palestinians would rise from the 69th, to meet at the 25th percentile.

Clearly, the separation keeps poor people away from rich people. Whether it increases or decreases conflict is a matter of debate.

Meanwhile

Meanwhile, the USA has its own enforced exclusion of poor people.

Photo of US/Tijuana border by Kordian from Flickr Creative Commons

Photo of US/Tijuana border by Kordian from Flickr Creative Commons.

The current crisis at the southern border of the USA mostly involves children from Guatemala, Honduras, and El Salvador. They don’t actually share a border with the USA, of course, but their region does, and crossing into Mexico seems pretty easy, so it’s the same idea.

To make a parallel comparison to Israel and the West Bank/Gaza, I just used Guatemala, which is larger by population than Honduras and El Salvador combined, and also closest to the USA. The economic gap between the USA and Guatemala is even larger than the Israeli/Palestinian gap. However, because the USA is 21-times larger than Guatemala by population, we could easily absorb the entire Guatemalan population without much damaging our national averages. Per capita income in the USA, for example, would fall only 4%, while rising more than 7-times for Guatemala (click to enlarge):

guatemalausa

This simplistic analysis yields a straightforward hypothesis: violence and military force at national borders rises as the income disparity across the border increases. Maybe someone has already tested that.

The demographic solution is obvious: open the borders, release the pressure, and devote resources to improving quality of life and social harmony instead of enforcing inequality. You’re welcome!

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What a recovery looks like (with population growth by age)

If you don’t account for population growth, I don’t get what you’re saying with these employment numbers. I’ll show a simple example, but first a little rundown on Friday’s jobs report.

Here is how CNN Money played the jobs report:

cnn-jobs

What does it mean, this loss and gain of jobs, returning finally to where we started? Four paragraphs under that happy headline, CNN did points out:

Given population growth over the last four years, the economy still needs more jobs to truly return to a healthy place. How many more? A whopping 7 million, calculates Heidi Shierholz, an economist with the Economic Policy Institute.

So what does “Finally!” mean? The Wall Street Journal ran the headline, “Jobs Return to Peak, but Quality Lags.” On 538 it was, “Women returned to prerecession levels of employment in 2013. Men remain hundreds of thousands of jobs in the hole:”

538-jobs

The Center on Budget and Policy Priorities showed how much better the previous recoveries were:

cbpp-jobs

That’s a good comparison. And CBPP mentioned population growth, too:

…payroll employment has finally topped its level at the start of the recession. Still, with essentially no net job growth since December 2007 but a growing working-age population, many more people today want to work but don’t have a job.

It’s not that no one mentions population growth, it’s that they still lead with the “top line” number. And they all have that horizontal line at the raw number of jobs when the recession started as the benchmark. I don’t know why.

Maybe in some crazy economics world the absolute number of jobs is what really matters for evaluating a recovery, and that explains the fixation on that horizontal line. From a social perspective what matters is the proportion of people with jobs. I could see the logic if you had a finite number of employers that never change, where you could literally count up the jobs at two points in time, and see who added and who subtracted from their payrolls (this is why retail chains report “same-store” trends, so the statistics aren’t confounded by the changing number of stores). But we have zillions of employers, constantly changing and moving around — largely in response to population changes. So that static image seems pointless.

In perspective

So here are some charts to put the recession and recovery in slightly better perspective. These all use the Bureau of Labor Statistics’ Current Population Survey from March 2003 to March 2013 (from IPUMS), the household survey used to track the labor force. I use ages 15 and older, and combine people in school (up to age 24) with those employed (not how most people do it, but a lot of people went to school, or stayed in school, because of the bad job market, and it doesn’t make sense to count them as not simply not employed). The survey excludes people in institutions, like prisons, and on-base military personnel.

To show the basic issue, here are the changes in the non-institutionalized population, age 15+, along with the number of them employed or in school — showing absolute changes relative to 2008, the peak employment year.

popjobs1

The 15+ population increased almost 12 million from 2008 to 2013. People employed or in school was not yet back to 2008 levels in March 2013. So a basic population adjustment is the least you can ask for (and we get that from the BLS with the employment-population ratio, which as of May was up less than one percent in the last 3.5 years, but it’s not the headline number).

What about age shifts? You don’t expect extreme age composition changes in 5 years, but there are different employment trends at different ages, so those affect how many employed people we are short. Here are the trends in work/school, by age and sex:

popjobs2

This makes it look like the 30-49s that are getting crushed. The 50+ community’s gains, however,are deceptive — their population is increasing. In fact, the population of people 30-49 declined 5% during this decade, while the population 50+ increased almost 30%. The younger people have increased their schooling rates, but their population has also grown. If you look at the employment/school rates, you see that among men, it is the younger groups that have done worst:

popjobs3

Women clearly are doing better (partly because in the 20-29 range they’re going to school more). It is amazing that employment rates didn’t fall at all over age 60. This could be because the population increase in that group is all in Baby Boomers just hitting their sixties, but I reckon it’s also people delaying retirement compensating for unemployment.

Now that we have age-specific work/school rates, and population changes, we can easily calculate how many people in each age group would have to be in work/school to get to the number implied by applying the peak-year 2008 rates to the population in each year. Sorry this one is so ugly: I made the last bar for each group pink to show the bottom line, where each group stands in 2013 relative to 2008:

popjobs4

Worst off are the 20-something men, down more than a million worker/students in 2013. Interestingly, women are only better off in the 20-something and 50+ ranges.

Finally, if you sum these figures you get the total, age-adjusted losses, by sex since 2008, as of March 2013:

popjobs5

And that’s your bottom line. The job/school losses stood at 3.3 million for men and 2.4 million for women as of March 2013.*

Really, there are no huge surprises here. In fact, the total population change is not a bad rough adjustment, especially for the short term. But there are some interesting nuances here. And with all the data and computers we have these days, let’s adjust for age and sex.

*I don’t say that’s how many “jobs” we need, because I don’t think “jobs” exist — are created, destroyed, shipped overseas, etc. I think there are employed people, people getting jobs, losing jobs, etc. I don’t see how a “job” exists absent a worker in it (and no, a listing is not a job until they fill it). So we don’t need to “create jobs” after a recession, what we need to do is “hire people.” Pet peeve.

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Changing Hispanic racial identity, or not

Hector Cordero-Guzman called my attention to a controversy over Hispanics changing their racial identities. Here is a quick rehash and a few comments. (Spoiler: the New York Times ran a bad story.)

At the Population Association of America, Carolyn Liebler, a sociologist at the University of Minnesota, and James Noon, who works on administrative records at the Census Bureau, presented preliminary results from a comparison of individual race/ethnic responses to the 2000 and 2010 Decennial Censuses. After analyzing millions of individual Census responses, they reported in their abstract that 6% of people changed their race or Hispanic origin classification between 2000 and 2010.

Details of the analysis apparently are not publicly available, but D’Vera Cohn, a writer at the Pew Research Center, reported on their findings, under the headline, “Millions of Americans changed their racial or ethnic identity from one census to the next.” Is this a lot of change? It’s hard to say without a comparison (and without the analysis details). “Millions” does not really mean “a lot,” but it sounds like it does. If the Census race/ethnic identity questions don’t fit people’s self-concept very well then a certain amount of bouncing around is to be expected.

The focus was on Hispanics, whose place in the racial classification scheme is squishy (including immigrants who came at different ages from countries with different racial schemes and ancestral origins, living in different parts of the country with different racial attitudes, some concentrated in dense communities and some dispersed, some economically marginalized and some much more upwardly mobile, etc.). According to D’vera Cohn, 2.5 million Hispanics were “some other race” in 2000 and “white” in 2010, while 1.3 million were “white” in 2000 and “some other race” in 2010.

I might conclude from that that it’s messy and the categories don’t work very well. But it’s also possible that this reflects fluid identities, and people actually change how they see themselves in a systematic way over time. The PAA abstract says “responses and corresponding identities can change over time,” which leaves open the possibility that the change is in measurement in addition to identity, but the hypothesis they suggest are about identity (hypothesizing that women, young people, and people in the West have more complex or less stable identities).

Identity shift is how New York Times Upshot writer Nate Cohn interpreted the D’Vera Cohn report. Under the headline, “More Hispanics Declaring Themselves White,” he converted that bidirectional flow into “net 1.2 million” changing from “some other race” to “white,” and proceeded to run away with the implications. It’s a good example of using any number greater than zero to confirm something you already believe. For example, he wrote:

The data also call into question whether America is destined to become a so-called minority-majority nation, where whites represent a minority of the nation’s population. Those projections assume that Hispanics aren’t white, but if Hispanics ultimately identify as white Americans, then whites will remain the majority for the foreseeable future.

Hm. The “net” flow from “some other race” to “white” is 1.2 million. That is 3% of the 2000 Hispanic population, or 2% of the 2010 population. So even if it’s truly an identity change, does that save the White majority in the long run?

Anyway, as Cordero-Guzman points out in a detailed discussion, referring to a post by Manuel Pastor, the Census questions changed between 2000 and 2010, with Census adding, in bold, “For this census, Hispanic origins are not races” to the form in 2010. Since many Hispanics write “Hispanic” under “some other race,” this probably discouraged them from choosing “some other race” in 2010.

Cordero-Guzman also points out that the context in which the question is asked (and in which the respondents live) is important. For example, 82% of Puerto Ricans on the island use “white” on the American Community Survey, while in New York City only 45% do. Does their identity — in the sense of how they really think of themselves — change when they are in New York, or do they interpret the question differently because they are answering a question in a different social setting? You can’t quantify that difference, probably, but I wouldn’t assume it’s just an identity change.

In a follow-up post, Nate Cohn acknowledges the wording changes — “an important detail” — but returns to the assimilation-upward mobility story. He should have just acknowledged that he jumped to conclusions in the first post and overreached in the race to produce an important, “data-driven” post. (Nate Cohn may have consulted actual experts, but if he did he didn’t include them in the post. It’s just data, I guess.)

The information economy did it

There is a lesson here in the new information economy. Academic conferences used to be less in the public eye. A preliminary analysis, shared with other researchers, then a Pew writer posts on the results, and the Times splashes them all over, all before a paper is even available. I think the Times story is basically wrong — the data as reported are not independent evidence of “assimilation.” (So, the person with the biggest megaphone was the person who was most wrong — surprise!) But the analysis could well be an important piece of research in a larger literature, and I think it’s good to present preliminary research at conferences. You can’t stop reporters from racing to be wrong, but I do think it would be better to distribute the paper publicly when it’s presented.

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US teen birth rates remain high, and they’re not falling for the reasons you’ve heard

Everyone is excited by the decline in the teen birth rate in the US. But And here are a few things you should know about it.

This chart shows the birth rates for women ages 15 to 19 in 192 countries, plus the world and the UN-defined rich countries, for 1991 and 2011. Dots below the black line show countries where the teen birth rate fell. The red line shows the overall relationship between 1991 and 2011. Dots below the red line had greater than expected reduction in teen births.

teen births global

Source: My graph United Nations data.

The chart shows four things:

1. Teen birth rates are falling globally. From 1991 to 2011, the birth rate for women ages 15 to 19 fell from 65 to 46 births per 1,000 women worldwide.

2. US has higher teen birth rates than any other rich country. At 33 per 1,000, the US has more teen births than Pakistan (28), but fewer than India (36). For high income countries, by the UN definition, the rate is 19. The rate for the Euro area is 7.

3. The teen birth rate is falling faster in the US than in the world overall. The world rate fell 29% from 1991 to 2011, while the drop in the US was 44%.

In the US, there are a lot of factors related to falling teen births. But they’re mostly about how it’s happening, not why it’s happening. For example, Vox published a list of factors, as did Pew before them, that are reasonable: the recession, more birth control, more Medicaid money for family planning, cultural pressure, and less sex.

But to understand why this is happening, you have to stop thinking about teenagers as some sort of separate subspecies. They are just young women. Soon they will be in their 20s. The same women! So the short answer for why falling teen birth rates happening is this:

4. Teen birth rates in the US are falling because women are postponing their births generally.

You can see this if you line up teens next to women of other ages. Here are the changes in birth rates for women, by age, from 1989 to 2012.

birthratechangebyage

Source: My graph from National Center for Health Statistics data.

See how the trend for the last decade is parallel for 15-17, 18-19, and 20-24? As those rates fell, birth rates rose for the 30+ community. The younger women are, the fewer births they’re having; the older they are, the more births they’re having. Teenage women are women! They do it for all the reasons it’s happening around the world: some because they are delaying marriage, some to pursue education and careers, some to see the world, and so on.

Here is another way to look at this. Here are the 50 US states, from the 2000-2012 American Community Survey. This shows that states with lower teen birth rates (those are per 100, on the y-axis), have higher birth rates for 25-34 year-old women relative to 20-24 year-old women. I’ll explain:

teenbirthstates

Teen births rates and the ratio of teen birth rates ages 25-34 / 20-24. US states, 2010-2011

Where more women have children ages 25-34 relative to 20-24, there are fewer teen births. So, in Alabama, about 3% of women 15-19 had a baby per year, and in that state the birth rates are about the same for women 25-34 as 20-24. Alabama is an early-birth state. But in New Hampshire, only 1% of teens had a baby, and women 25-34 were almost 2.5-times more likely to have a baby than women 20-24. New Hampshire is a late-birth state. What’s happening with teens reflects what’s happening with older women.

To some significant degree, it’s not about teenagers, it’s about women delaying births.* I would love it if reporting on teen births would always compare them to older women.

*Notice I didn’t just exaggerate and say, “it’s not about teenagers.” I added “to some significant degree.” That’s the difference between a post that is selling you (your clicks) to someone versus a post that’s trying to explain things as clearly as possible.

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Check that: Most marrying people are remarrying above age 31

The other day I wrote that the majority of people marrying over age 35 have been married before. That is true, but because of the way I handled the age categories it’s not specific enough. In fact, the majority of men marrying over age 30, and the majority of women marrying over age 28, have been married before.

Here are the details, in two charts, both using marital events data from the 2012 American Community Survey from IPUMS.org. The first shows the breakdown between first-married and previously-married people marrying at each age. It is not until age 40 for men, and age 38 for women, that previously-married people become the majority marrying at each age. These proportions reach two thirds in the mid-40s and surpass 80% by age 52:

timesmarriedmarrying-area

But the percent remarrying at or above a given age is higher. Here is that pattern, showing that we enter majority-remarried territory at 31 for men and 29 for women:

timesmarriedmarrying-lines

The rates of remarriage at a given age maybe matter more practically, but this is a neat way to look at it.

Note there is no demographic reason that these patterns must hold. If remarriage were taboo or more restricted this would not be the case. Being ever married cannot be revoked (unless people lie to the Census Bureau), so the percent ever-married should never decline for a cohort (unless the ever-married have much higher mortality or emigrate more than the never-married, which is very unlikely). But ever-married proportions for the population don’t have to rise with age in a given cross-section, even if you don’t just look at people marrying right now. If marriage were becoming more common on a cohort basis, for example (which it is not), you could see higher ever-married rates among young people than among old people.

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Most people marrying over age 35 have been married before

Among people who got married in the past year, more than half of those ages 35-44 had been married before. For those ages 45 and older, only 21% are marrying for the first time — and almost 30% have been married twice (or more).

timesmarriedmarrying

Source: My analysis of ACS data from IPUMS.org.

 

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Sexual minority counts

One of the big happenings at the Population Association of American (PAA) conference, just completed, was news of progress toward collecting better data on sexual diversity.

Photo by Philip Cohen from Flickr Creative Commons

Photo of PAA 2014 by Philip Cohen from Flickr Creative Commons

Call it weakness if you like, but in this area I am prone to viewing modernity as a march of progress from a dark past toward a half-full glass of bright future, with popular politics driving widening notions of human rights, motivating legal reforms, compelling the adoption of state bureaucracies to progressive social reality, and gradually incorporating us into a new world order more or less of our own creation.

That last part – about the bureaucracies incorporating the public – might not be the most complicated, but it is still pretty thorny. (And from here till the next subhead it gets technical.)

Good news bad news

The good news is that we have great new data collections coming along. Virginia Cain from the National Center for Health Statistics reported on their new sexual orientation question for the National Health Interview Survey, the largest federal health survey (the paper doesn’t seem to be available yet). This is already yielding important data on health disparities for sexual minorities, which is vital for policy responses to inequality.

Tim Vizard from the UK Office of National Statistics also reported on his agency’s new sexual identity question, which has been tested for several years on a few hundred thousand people each year. The latest numbers show 1.5% of adults self-identifying as lesbian, gay, or bisexual. They get these low numbers because they ask a very simple, narrow question, only on sexual identity rather than sexual attraction or sexual behavior (see other studies for the range of estimates).* Importantly, less than 4% of the UK respondents are refusing to answer, and the question is not affecting overall response rates – two big fears in the statistical agencies that appear to be receding with these and other results. Here’s how they ask it (semi-confidentially, so that in theory a husband and wife taking the survey together could both tell the interviewer they’re gay without either knowing what the other said):

lgbt1

The other good news is that the U.S. Census Bureau is making great strides (which I first praised here), on several tracks. First, they are working on the same-sex married couple data from the American Community Survey (ACS). At present they only release aggregate estimates of same-sex couples, differentiating between those that are married versus cohabiting (explained here).

A big reason we don’t have more data is the bad news: In another paper (just an abstract is posted, but you can ask the authors for a copy), Census analysts Daphne Lofquist and Jamie Lewis reported on their investigation into possible errors in the same-sex couple data the ACS has collected.

The background is that in a 2011 paper (linked here) Census analysts showed that a lot of seemingly same-sex couples were actually different-sex couples in which someone’s sex was miscoded.** If even a tiny percentage of different-sex couples make a mistake on the form – say, 1-in-1000 – then you would roughly double the number of same-sex couples. And they do. The paper used name-gender associations to reveal that, for example, in Texas 29% of supposedly male-male couples had one partner with a name that was used by women 95% of the time in that state – probably women accidentally marked as male.

But that 95% cutoff is a conservative estimate of the error. In the new analysis Lofquist and Lewis went further and checked same-sex couples against their Social Security records to see what sex they had recorded there. The result was shocking: 72.5% of the same-sex couples had a member whose sex didn’t match the Social Security record. Yes, some people change their sex/gender, and some people’s Social Security Records are wrong, but not that many. The much more likely culprit is simply a tiny number of straight people mismarking the sex box (there are some other technical possibilities, too).

The great thing about just asking people their marital status and sex is that you can count gay and lesbian couples without changing anything about the form (such as asking about sexual identity or orientation). That’s what all the people want who think I’m backward for worrying about couple-sex gender terminology. “C’mon!” they say, “Why do you have to label marriage as homogamous or heterogamous – just call it marriage!” Maybe someday, but at the moment that approach is producing an accuracy-crushing level of noise in the same-sex couple data.

Fortunately, Census is also moving forward with other improvements to fix this. The most important change is probably to the basic relationship question, which will soon look something like this, with couples labeled “opposite-sex” or “same-sex,” and the gender-neutral “spouse” added beside “husband/wife.” This will allow Census to check those couples that are reported as married to see if their same/opposite relationship identification matches what they reported for their sexes:

lgbt2

If we end up with a question like that, which seems most likely (the Census testing and development is quite far along), then we should be able to much more reliably identify same-sex couples (both married and cohabiting).

We’ll get used to this

That proposed new relationship question has 17 categories. That’s a long way from these six, in 1960 (the whole series of Census forms is here):

1960relationships

That goes to show you that family diversity is a state of collective mind as well as a structural reality. Building bureaucratic bins into which we pour data describing the various aspects of our lives is one of the defining elements of modern life. Eventually, I am pretty sure people will become disciplined by the new bureaucratic reality, and identities will calcify around checkboxes. That’s life under the modern state. (Even most haters, once they realize the data is being collected, will want to answer the questions accurately so they don’t get counted as gay – although, just as a few people refuse to answer race questions, there will be holdouts.)

* Identifying transgender people is much more complicated and difficult. The number of required questions and categories increases as the size of the groups in question grows smaller. This is feasible for smaller, more targeted surveys, but not in the immediate cards for the big ones (see Gary Gates’s presentation at PAA for more on this).

** I’m pretty sure Gary Gates was the first person to identify this problem, but can’t remember which paper it was in.

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How many WWII war brides are still living?

Maybe a couple thousand.

European war brides arriving in New York, 1945

European war brides arriving in New York, 1945

Someone should do some new interviews with the World War II “war brides,” because there aren’t very many still living.

I count 1,195 still married and living with their husbands. That means there might be something like 2,000 living if you count widows and those who have remarried. We don’t know exactly how many there were, but various sources put the number at 60,000 or more.

Here’s how I got that current number, using the American Community Survey three-year file, 2010-2012. It’s all the couples who met the following conditions:

  • Married, spouse-present
  • She was born outside the U.S.
  • He was born in the U.S.
  • He is a WWII-era veteran
  • They were married in the years 1941-1945
  • She immigrated in or after the year of their marriage

It’s a pretty simple set of rules.

Some caveats: This doesn’t include any widows or widowers, just those still married (otherwise the ACS doesn’t have any spouse information). I didn’t set a requirement that she be born in a place where American soldiers were during the war (I don’t know all the places they were). I don’t know that all of the WWII-era veterans served outside the U.S. So some of these might not be real war brides, in the sense of women who met and married American military men outside the U.S. during a war.

Still, I think the formula works well. These are the women it turned up:

  • 84% immigrated in 1945 or 1946
  • The age range is 82-94, with a median of 85
  • About two-thirds were under age 20 when they married
  • 61% from the United Kingdom (mostly England)
  • 11% from elsewhere in Western Europe (France, Belgium, Italy)
  • 7% from Eastern Europe (Czechoslovakia, Yugoslavia)
  • The remaining 20% from Canada, Australia/New Zealand, Israel/Palestine, Japan, other)

If you follow my suggestion of finding and interviewing these women or their husbands, here are some other sources you might use:

 

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Different divorce rates

Deadline crush, not getting out the posts I want to. So here instead is one thing I was planning to write about but didn’t really yet.

Photo by Dan Bluestein from Flickr Creative Commons

What’s the rate? Photo by Dan Bluestein from Flickr Creative Commons

I’ve written about divorce quite a bit on here, including on the mess of our official statistics. Now Sheela Kennedy and Steven Ruggles have a (paywalled) paper in the January issue of Demography called, “Breaking Up Is Hard to Count: The Rise of Divorce in the United States, 1980–2010.” Because of the paywall and the obscure academic journal, I thought I had some time to write about it, but it’s been reported on Wonkblog and and other places, so no point in waiting.

The headline is, “divorce is actually on the rise.” It’s risen when they age-standardize the trend, but it’s complicated: “Divorce rates have doubled over the past two decades among persons over age 35. Among the youngest couples, however, divorce rates are stable or declining.” The interpretation is not as simple as, “they have a better measure.”

Meanwhile, I was quoted in a Wall Street Journal story about some TV show, and I let slip my multiple-decrement lifetable version of the current divorce rate. This hasn’t been finished, much less peer-reveiwed, but I’m pretty confident about the basic result. I wrote to the reporter, who asked me for the divorce rate:

As for divorce rates, it’s hard to be definitive because there is no one answer. One answer is: In 2012 there were 19 divorces for every 1000 people who were married (my calculation from the 2012 ACS).

However, what most people want to know is what percentage of people who get married will end up getting divorced. There is no official estimate of this because it involves a guess about the future. We can estimate divorce like we estimate life expectancy — it’s not the actual prediction of how long people will live, it’s how long they would live on average if they lived through the risks of most recent year over and over again their whole lives. (Technically, it’s a projection, not a prediction.) Anyway, using that method, I estimate that about 50% of couples who married in 2012 would eventually divorce (with the rest of the marriages ending with someone’s death).

In her story, of course, that became, simply, “And about half of those who married in 2012 will eventually divorce.”

This method, which I got from this old Sam Preston paper, combines mortality rates and death rates to project how many people are lucky enough to die before divorcing at current rates. (Hence “multiple-decrement,” the demographers’ dry way of saying, “there are only two ways out of this.”) When he applied the method, with much cruder data from 1973, incidentally, he got a 43% divorce rate, which was much higher than the rates floating around at the time, and would have made big news in the blogosphere if there had been one.

More on this eventually.

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What’s in a ratio? Teen birth and marriage edition

Even in our post-apocalypse world, births and marriages are still related, somehow.

Some teenage women get married, and some have babies. Are they the same women? First the relationship between the two across states, then a puzzle.

In the years 2008-2012 combined, 2.5 percent of women ages 15-19 per year had a baby, and 1 percent got married. That is, they were reported in the American Community Survey (IPUMS) to have given birth, or gotten married, in the 12 months before they were surveyed. Here’s the relationship between those two rates across states:

teenbirthmarriage1The teen birth rate  ranges from a low of 1.2 percent in New Hampshire to 4.4 percent in New Mexico. The teen marriage rate ranges from .13 percent in Vermont to 2.3 percent in Idaho.

But how much of these weddings are “shotgun weddings” — those where the marriage takes place after the pregnancy begins? And how many of these births are “gungo-ho marriages” — those where the pregnancy follows immediately after the marriage? (OK, I made that term up.) The ACS, which is wonderful for having these questions, is somewhat maddening in not nailing down the timing more precisely. “In the past 12 months” is all you get.

Here is the relationship between two ratios. The x-axis is percentage of teens who got married who also had a birth (birth/marriage). On the y-axis is the percent of teens who had a birth who also got married (marriage/birth).

teenbirthmarriageIf you can figure out how to interpret these numbers, and the difference between them within states, please post your answer in the comments.

 

 

 

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